Afrânio Melo
Data Scientist
Rio de Janeiro, Brazil
Summary
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Top SchoolAfrânio Melo is a data scientist at Petrobras with eight years of experience bridging chemical engineering and machine learning to monitor and predict industrial process behavior. Completing a doctorate at COPPE/UFRJ, he has driven research partnerships at EngePol that produced deployed industry tools (ProFal, SmartVent, BibMon, MemBRain) for fault prognosis, membrane modeling and data-driven dynamic analysis. He taught mass transfer and unit operations at UFRJ and long-running chemistry courses for CEDERJ, and also created a 13-hour Portuguese course on Data Science and ML for process industries. Comfortable moving research into production, he combines domain depth in separation processes with practical skills in statistical process monitoring and predictive maintenance. Outside engineering he pursues literary writing—winner of the Hernâni Cidade Prize—and is currently drafting his first novel, a detail that underscores his interdisciplinary curiosity and communication strengths.
7 years of coding experience
5 years of employment as a software developer
Doctor of Science, Chemical Engineering, Doctor of Science, Chemical Engineering at UFRJCreative Writing, Creative Writing at PUCRS
English, Spanish
Github Skills (12)
process-monitoring10
github-pages10
anomaly-detection10
jekyll10
responsive10
jekyll-theme10
pca9
machine-learning9
time-series8
data-science8
python8
scikit-learn7
Programming languages (3)
Github contributions (5)
afraniomelo/KydLIB
Aug 2022 - Feb 2023
Contributions:3 releases, 13 commits, 12 pushes in 5 months
petrobras/BibMon
May 2024 - Jan 2025
Python package that provides predictive models for fault detection, soft sensing, and process condition monitoring.
Contributions:20 releases, 84 PRs, 56 pushes in 7 months
echo-state-networkfault-detectionfault-diagnosismachine-learningneural-networks
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